Python autograd.Variable类代码示例

def train(ep):
    model.train()
    total_loss = 0
    count = 0
    train_idx_list = np.arange(len(X_train), dtype="int32")
    np.random.shuffle(train_idx_list)
    for idx in train_idx_list:
        data_line = X_train[idx]
        x, y = Variable(data_line[:-1]), Variable(data_line[1:])
        if args.cuda:
            x, y = x.cuda(), y.cuda()

        optimizer.zero_grad()
        output = model(x.unsqueeze(0)).squeeze(0)
        loss = -torch.trace(torch.matmul(y, torch.log(output).float().t()) +
                            torch.matmul((1 - y), torch.log(1 - output).float().t()))
        total_loss += loss.data[0]
        count += output.size(0)

        if args.clip > 0:
            torch.nn.utils.clip_grad_norm(model.parameters(), args.clip)
        loss.backward()
        optimizer.step()
        if idx > 0 and idx % args.log_interval == 0:
            cur_loss = total_loss / count
            print("Epoch {:2d} | lr {:.5f} | loss {:.5f}".format(ep, lr, cur_loss))
            total_loss = 0.0
            count = 0

    def predict(self, dataset):
        """Predict target for dataset.

        Parameters:
        ----------
        dataset (dict): dictionary with the testing dataset -
        X_wide_test, X_deep_test, target

        Returns:
        --------
        array-like with the target for dataset
        """

        X_w = Variable(torch.from_numpy(dataset.wide)).float()
        X_d = Variable(torch.from_numpy(dataset.deep))

        if use_cuda:
            X_w, X_d = X_w.cuda(), X_d.cuda()

        # set the model in evaluation mode so dropout is not applied
        net = self.eval()
        pred = net(X_w,X_d).cpu()
        if self.method == "regression":
            return pred.squeeze(1).data.numpy()
        if self.method == "logistic":
            return (pred > 0.5).squeeze(1).data.numpy()
        if self.method == "multiclass":
            _, pred_cat = torch.max(pred, 1)
            return pred_cat.data.numpy()

    def forward(self, k, x, logposterior):
        '''
        k: number of samples
        x: [B,X]
        logposterior(z) -> [P,B]
        '''

        self.B = x.size()[0]
        self.P = k

        #Encode
        out = x
        for i in range(len(self.encoder_weights)-1):
            out = self.act_func(self.encoder_weights[i](out))
        out = self.encoder_weights[-1](out)
        mean = out[:,:self.z_size]
        logvar = out[:,self.z_size:]

        #Sample
        eps = Variable(torch.FloatTensor(k, self.B, self.z_size).normal_().type(self.dtype)) #[P,B,Z]
        z = eps.mul(torch.exp(.5*logvar)) + mean  #[P,B,Z]
        logqz = lognormal(z, mean, logvar) #[P,B]

        logdetsum = 0.
        for i in range(self.n_flows):

            z, logdet = self.norm_flow(self.params[i],z)
            logdetsum += logdet


        return z, logqz-logdetsum

    def forward_single_image_tensor(self, img_tensor):
        """
        Simple forward pass on the network.

        Normalize the image if we are in TEST mode
        If we are in TRAIN mode then assume the dataset object has already normalized
        the image

        :param img_tensor: torch.FloatTensor with shape [3,H,W]
        :type img_tensor:
        :return: torch.FloatTensor with shape  [H, W, D]
        :rtype:
        """

        assert len(img_tensor.shape) == 3


        # transform to shape [1,3,H,W]
        img_tensor = img_tensor.unsqueeze(0)

        # The fcn throws and error if we don't use a variable here . . .
        # Maybe it's because it is in train mode?
        img_tensor = Variable(img_tensor.cuda(), requires_grad=False)
        res = self.forward(img_tensor) # shape [1,D,H,W]
        # print "res.shape 1", res.shape


        res = res.squeeze(0) # shape [D,H,W]
        # print "res.shape 2", res.shape

        res = res.permute(1,2,0) # shape [H,W,D]
        # print "res.shape 3", res.shape

        return res

def generate(model, start_words, ix2word, word2ix, prefix_words=None):
    """
    给定几个词，根据这几个词接着生成一首完整的诗歌
    start_words：u'春江潮水连海平'
    比如start_words 为 春江潮水连海平，可以生成：

    """
    results = list(start_words)
    start_word_len = len(start_words)
    # 手动设置第一个词为<START>
    input = Variable(t.Tensor([word2ix['<START>']]).view(1, 1).long())
    if opt.use_gpu: input = input.cuda()
    hidden = None

    if prefix_words:
        for word in prefix_words:
            output, hidden = model(input, hidden)
            input = Variable(input.data.new([word2ix[word]])).view(1, 1)

    for i in range(opt.max_gen_len):
        output, hidden = model(input, hidden)

        if i < start_word_len:
            w = results[i]
            input = Variable(input.data.new([word2ix[w]])).view(1, 1)
        else:
            top_index = output.data[0].topk(1)[1][0]
            w = ix2word[top_index]
            results.append(w)
            input = Variable(input.data.new([top_index])).view(1, 1)
        if w == '<EOP>':
            del results[-1]
            break
    return results

def _pad_packed_sequence(sequence, batch_first=False, padding_value=0):
    var_data, batch_sizes = sequence
    max_batch_size = int(batch_sizes[0])
    output = var_data.data.new(len(batch_sizes), max_batch_size, *var_data.size()[1:]).fill_(padding_value)
    output = Variable(output)

    lengths = []
    data_offset = 0
    prev_batch_size = int(batch_sizes[0])
    prev_i = 0
    for i, batch_size in enumerate(batch_sizes.tolist() + [0]):
        if batch_size != prev_batch_size:
            l = prev_batch_size * (i - prev_i)
            tmp = var_data[data_offset:data_offset + l]
            output[prev_i:i, :prev_batch_size] = tmp.view(i - prev_i, prev_batch_size, *tmp.size()[1:])
            data_offset += l
            prev_i = i
        dec = prev_batch_size - batch_size
        if dec > 0:
            lengths.extend((i,) * dec)
        prev_batch_size = batch_size

    lengths.reverse()

    if batch_first:
        output = output.transpose(0, 1)
    # This Variable doesn't actually have any history (well,
    # technically it does; it's just untracked), it is purely here to
    # make ONNX export easier. That is to say, from an autodiff
    # standpoint this doesn't make any sense.
    return output, Variable(torch.LongTensor(lengths))

    def probs(self, generator, outputs, vocab_pointer_switches, context_question_switches, 
        context_attention, question_attention, 
        context_indices, question_indices, 
        oov_to_limited_idx):

        size = list(outputs.size())

        size[-1] = self.generative_vocab_size
        scores = generator(outputs.view(-1, outputs.size(-1))).view(size)
        p_vocab = F.softmax(scores, dim=scores.dim()-1)
        scaled_p_vocab = vocab_pointer_switches.expand_as(p_vocab) * p_vocab

        effective_vocab_size = self.generative_vocab_size + len(oov_to_limited_idx)
        if self.generative_vocab_size < effective_vocab_size:
            size[-1] = effective_vocab_size - self.generative_vocab_size
            buff = Variable(scaled_p_vocab.data.new(*size).fill_(EPSILON))
            scaled_p_vocab = torch.cat([scaled_p_vocab, buff], dim=buff.dim()-1)

        p_context_ptr = Variable(scaled_p_vocab.data.new(*scaled_p_vocab.size()).fill_(EPSILON))
        p_context_ptr.scatter_add_(p_context_ptr.dim()-1, context_indices.unsqueeze(1).expand_as(context_attention), context_attention)
        scaled_p_context_ptr = (context_question_switches * (1 - vocab_pointer_switches)).expand_as(p_context_ptr) * p_context_ptr

        p_question_ptr = Variable(scaled_p_vocab.data.new(*scaled_p_vocab.size()).fill_(EPSILON))
        p_question_ptr.scatter_add_(p_question_ptr.dim()-1, question_indices.unsqueeze(1).expand_as(question_attention), question_attention)
        scaled_p_question_ptr = ((1 - context_question_switches) * (1 - vocab_pointer_switches)).expand_as(p_question_ptr) * p_question_ptr

        probs = scaled_p_vocab + scaled_p_context_ptr + scaled_p_question_ptr
        return probs

    def sample(self, mu, logvar, k):

        # print (mu)
        # print (logvar)


        if torch.cuda.is_available():
            eps = Variable(torch.FloatTensor(k, self.B, self.z_size).normal_()).cuda() #[P,B,Z]

            # print (mu.size())
            # print (logvar.size())
            # print (eps.size())

            z = eps.mul(torch.exp(.5*logvar)) + mu  #[P,B,Z]
            logpz = lognormal(z, Variable(torch.zeros(self.B, self.z_size).cuda()), 
                                Variable(torch.zeros(self.B, self.z_size)).cuda())  #[P,B]



            # logqz = lognormal(z, mu, logvar)

            logqz = lognormal(z, Variable(mu.data), Variable(logvar.data))



        else:
            eps = Variable(torch.FloatTensor(k, self.B, self.z_size).normal_())#[P,B,Z]
            z = eps.mul(torch.exp(.5*logvar)) + mu  #[P,B,Z]
            logpz = lognormal(z, Variable(torch.zeros(self.B, self.z_size)), 
                                Variable(torch.zeros(self.B, self.z_size)))  #[P,B]
            logqz = lognormal(z, mu, logvar) 
        return z, logpz, logqz

    def __val(self):
        """
          Validation function during the train phase.
        """
        self.seg_net.eval()
        start_time = time.time()

        for j, data_tuple in enumerate(self.val_loader):
            # Change the data type.
            inputs = Variable(data_tuple[0].cuda(async=True), volatile=True)
            targets = Variable(data_tuple[1].cuda(async=True), volatile=True)
            # Forward pass.
            outputs = self.seg_net(inputs)
            # Compute the loss of the val batch.
            loss_pixel = self.pixel_loss(outputs, targets)
            loss = loss_pixel

            self.val_losses.update(loss.data[0], inputs.size(0))

            # Update the vars of the val phase.
            self.batch_time.update(time.time() - start_time)
            start_time = time.time()

        self.module_utilizer.save_net(self.seg_net, self.iters)
        # Print the log info & reset the states.
        Log.info(
            'Test Time {batch_time.sum:.3f}s, ({batch_time.avg:.3f})\t'
            'Loss {loss.avg:.8f}\n'.format(
            batch_time=self.batch_time, loss=self.val_losses))
        self.batch_time.reset()
        self.val_losses.reset()
        self.seg_net.train()

def random_batch(batch_size=3):
    input_seqs = []
    target_seqs = []

    # Choose random pairs
    for i in range(batch_size):
        pair = random.choice(pairs)
        input_seqs.append(indexes_from_sentence(input_lang, pair[0]))
        target_seqs.append(indexes_from_sentence(output_lang, pair[1]))

    # Zip into pairs, sort by length (descending), unzip
    seq_pairs = sorted(zip(input_seqs, target_seqs), key=lambda p: len(p[0]), reverse=True)
    input_seqs, target_seqs = zip(*seq_pairs)

    # For input and target sequences, get array of lengths and pad with 0s to max length
    input_lengths = [len(s) for s in input_seqs]
    input_padded = [pad_seq(s, max(input_lengths)) for s in input_seqs]
    target_lengths = [len(s) for s in target_seqs]
    target_padded = [pad_seq(s, max(target_lengths)) for s in target_seqs]

    # Turn padded arrays into (batch x seq) tensors, transpose into (seq x batch)
    input_var = Variable(torch.LongTensor(input_padded)).transpose(0, 1)
    target_var = Variable(torch.LongTensor(target_padded)).transpose(0, 1)

    if USE_CUDA:
        input_var = input_var.cuda()
        target_var = target_var.cuda()

    return input_var, input_lengths, target_var, target_lengths

    def update(self):
        

        next_value = self.actor_critic(Variable(self.rollouts.states[-1], volatile=True))[0].data

        self.rollouts.compute_returns(next_value, self.use_gae, self.gamma, self.tau)

        # values, action_log_probs, dist_entropy = self.actor_critic.evaluate_actions(
        #                                             Variable(self.rollouts.states[:-1].view(-1, *self.obs_shape)), 
        #                                             Variable(self.rollouts.actions.view(-1, self.action_shape)))


        values = torch.cat(self.rollouts.value_preds, 0).view(self.num_steps, self.num_processes, 1) 
        action_log_probs = torch.cat(self.rollouts.action_log_probs).view(self.num_steps, self.num_processes, 1)
        dist_entropy = torch.cat(self.rollouts.dist_entropy).view(self.num_steps, self.num_processes, 1)


        self.rollouts.value_preds = []
        self.rollouts.action_log_probs = []
        self.rollouts.dist_entropy = []

        advantages = Variable(self.rollouts.returns[:-1]) - values
        value_loss = advantages.pow(2).mean()

        action_loss = -(Variable(advantages.data) * action_log_probs).mean()

        self.optimizer.zero_grad()
        cost = action_loss + value_loss*self.value_loss_coef - dist_entropy.mean()*self.entropy_coef
        cost.backward()

        nn.utils.clip_grad_norm(self.actor_critic.parameters(), self.grad_clip)

        self.optimizer.step()

def F_affine2d(x, matrix, center=True):
    """
    2D Affine image transform on torch.autograd.Variable
    """
    if matrix.dim() == 2:
        matrix = matrix.view(-1,2,3)

    A_batch = matrix[:,:,:2]
    if A_batch.size(0) != x.size(0):
        A_batch = A_batch.repeat(x.size(0),1,1)
    b_batch = matrix[:,:,2].unsqueeze(1)

    # make a meshgrid of normal coordinates
    _coords = th_iterproduct(x.size(1),x.size(2))
    coords = Variable(_coords.unsqueeze(0).repeat(x.size(0),1,1).float(),
                    requires_grad=False)
    if center:
        # shift the coordinates so center is the origin
        coords[:,:,0] = coords[:,:,0] - (x.size(1) / 2. + 0.5)
        coords[:,:,1] = coords[:,:,1] - (x.size(2) / 2. + 0.5)

    # apply the coordinate transformation
    new_coords = coords.bmm(A_batch.transpose(1,2)) + b_batch.expand_as(coords)

    if center:
        # shift the coordinates back so origin is origin
        new_coords[:,:,0] = new_coords[:,:,0] + (x.size(1) / 2. + 0.5)
        new_coords[:,:,1] = new_coords[:,:,1] + (x.size(2) / 2. + 0.5)

    # map new coordinates using bilinear interpolation
    x_transformed = F_bilinear_interp2d(x, new_coords)

    return x_transformed

 def forward(self, inputs): # inputs (bs,words/sentence) 10,7
     bsz = inputs.size(0) # batch size might change
     if inputs.size(1) < 3: # padding issues on really short sentences
         pads = Variable(torch.zeros(bsz,3-inputs.size(1))).type(torch.LongTensor)
         inputs = torch.cat([inputs,pads.cuda()],dim=1)
     embeds = self.embeddings(inputs) # 10,h,300
     embeds = embeds.unsqueeze(3)
     embeds = embeds.permute(0,2,1,3)
     s_embeds = self.s_embeddings(inputs)
     s_embeds = s_embeds.unsqueeze(3)
     s_embeds = s_embeds.permute(0,2,1,3)
     out = torch.cat([embeds,s_embeds],dim=3)
     #print(out.size())
     fw3 = self.conv3(out) # 10,100,h,1
     fw5 = self.conv5(out) # 10,100,h,1
     fw7 = self.conv7(out) # 10,100,h,1
     out = torch.cat([fw3,fw5,fw7],dim=1)
     out = F.relu(out) # 10,300,h/3,1
     #out = self.avgpool(out)
     #out = F.relu(self.conv(out))
     #print(out.size())
     #out = out.view(bsz,n_featmaps*3,-1,2) # 10,300,7
     #print(out.size())
     out = self.maxpool(out) # 10,300,1,1
     out = out.view(bsz,-1) # 10,600
     out = self.dropout(out) # 10,2
     out = self.linear(out) # 10,2
     return out

    def update_parameters(self, batch):
        state_batch = Variable(torch.cat(batch.state))
        action_batch = Variable(torch.cat(batch.action))
        reward_batch = Variable(torch.cat(batch.reward))
        mask_batch = Variable(torch.cat(batch.mask))
        next_state_batch = Variable(torch.cat(batch.next_state))
        
        next_action_batch = self.actor_target(next_state_batch)
        next_state_action_values = self.critic_target(next_state_batch, next_action_batch)

        reward_batch = reward_batch.unsqueeze(1)
        mask_batch = mask_batch.unsqueeze(1)
        expected_state_action_batch = reward_batch + (self.gamma * mask_batch * next_state_action_values)

        self.critic_optim.zero_grad()

        state_action_batch = self.critic((state_batch), (action_batch))

        value_loss = F.mse_loss(state_action_batch, expected_state_action_batch)
        value_loss.backward()
        self.critic_optim.step()

        self.actor_optim.zero_grad()

        policy_loss = -self.critic((state_batch),self.actor((state_batch)))

        policy_loss = policy_loss.mean()
        policy_loss.backward()
        self.actor_optim.step()

        soft_update(self.actor_target, self.actor, self.tau)
        soft_update(self.critic_target, self.critic, self.tau)

        return value_loss.item(), policy_loss.item()

def show_result(num_epoch, show = False, save = False, path = 'result.png', isFix=False):
    z_ = torch.randn((5*5, 100)).view(-1, 100, 1, 1)
    z_ = Variable(z_.cuda(), volatile=True)

    G.eval()
    if isFix:
        test_images = G(fixed_z_)
    else:
        test_images = G(z_)
    G.train()

    size_figure_grid = 5
    fig, ax = plt.subplots(size_figure_grid, size_figure_grid, figsize=(5, 5))
    for i, j in itertools.product(range(size_figure_grid), range(size_figure_grid)):
        ax[i, j].get_xaxis().set_visible(False)
        ax[i, j].get_yaxis().set_visible(False)

    for k in range(5*5):
        i = k // 5
        j = k % 5
        ax[i, j].cla()
        ax[i, j].imshow((test_images[k].cpu().data.numpy().transpose(1, 2, 0) + 1) / 2)

    label = 'Epoch {0}'.format(num_epoch)
    fig.text(0.5, 0.04, label, ha='center')
    plt.savefig(path)

    if show:
        plt.show()
    else:
        plt.close()